Whilst the electrocardiogram (ECG) is an essential tool for diagnosing cardiac electrical abnormalities, its characteristics are dependent on anatomical variability. Specifically variation in torso geometry affects relative positions of the leads with respect to the heart. We propose a novel pipeline that uses standard cardiac magnetic resonance images to reconstruct the torso and heart, and recreate the ECG considering torso and cardiac anatomy. This requires automated extraction of the torso contours. Our method combines an initial u-net segmenter with a second network that refines contours and removes spurious segments. The networks were evaluated on a cross validation study dataset and an independent test set. The use of two-channel input, including both original image and initial segmentation, in the refinement network significantly improved performance on the independent test set, reducing the Hausdorff distance from 9.1 pixels to 4.3 pixels and increasing Dice coefficient from 0.75 to 0.93. Clinical Relevance- This method can be utilized to allow ECG simulations with personalized torso geometry which has previously been demonstrated to significantly effect QRS parameters. A clinical tool can be developed using this method that accounts for torso geometry in ECG interpretation.